High-Dimensional Categorical Visualization is a methodological framework in data visualization that enables the representation and exploration of categorical data with a large number of categories or dimensions by employing dimensionality-aware techniques to preserve structural relationships and enhance interpretability.
9
Publications
342
Citations
15
Authors
10
Institutions
Publications per year
2017–2026
1
Publications
9
Interactive Data Visualization Using<b>Mondrian</b>
Martin Theus · Journal of Statistical Software · 2002 · 107 citations · Full text
Probabilistic Visualisation of High-Dimensional Binary Data
Michael E. Tipping · 1998 · 54 citations
1–5 of 9
Authors
15
- MF
2
h-index
- ZY
1
h-index
- AB
1
h-index
- RK
1
h-index
- CZ
1
h-index
1–5 of 15
Institutions
10
University of North Carolina at Charlotte
Charlotte, United States
2 h-index
5 publications
70 citations
Texas A&M University
College Station, United States
1 h-index
1 publications
14 citations
University of Auckland
Auckland, New Zealand
1 h-index
1 publications
16 citations
Microsoft (United States)
Redmond, United States
1 h-index
1 publications
54 citations
Stony Brook University
Stony Brook, United States
1 h-index
3 publications
54 citations
York University
Toronto, Canada
1 h-index
1 publications
79 citations
University of Rochester
Rochester, United States
1 h-index
1 publications
13 citations
Mitchell Institute
Portland, United States
1 h-index
1 publications
14 citations
1–8 of 10
Venues
- Journal of Computational and Graphical Statistics
JCGS · 2 publications · 106 citations
2
h-index
- IEEE Transactions on Visualization and Computer Graphics
TVCG · 1 publications · 18 citations
1
h-index
- PLoS ONE
1 publications · 14 citations
1
h-index
- Visual Informatics
1 publications · 14 citations
1
h-index
- Journal of Statistical Software
JSS · 1 publications · 107 citations
1
h-index
1–5